# Clinical Trial Active Learning

arXiv: https://arxiv.org/abs/2307.11209 · HTML: https://mlsanity.com/paper/2307.11209/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [Positive-Congruent Training: Towards Regression-Free Model Updates](https://mlsanity.com/paper/2011.09161/) (2020)
- [OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics](https://mlsanity.com/paper/2209.11195/) (2022)
- [Gaussian Switch Sampling: A Second Order Approach to Active Learning](https://mlsanity.com/paper/2302.12018/) (2023)
- [Patient Aware Active Learning for Fine-Grained OCT Classification](https://mlsanity.com/paper/2206.11485/) (2022)
- [Clinically Labeled Contrastive Learning for OCT Biomarker Classification](https://mlsanity.com/paper/2305.15154/) (2023)
- [Relative Afferent Pupillary Defect Screening through Transfer Learning](https://mlsanity.com/paper/1908.02300/) (2019)
- [Multi-disciplinary fairness considerations in machine learning for clinical trials](https://mlsanity.com/paper/2205.08875/) (2022)
- [Multi-Modal Learning Using Physicians Diagnostics for Optical Coherence Tomography Classification](https://mlsanity.com/paper/2203.10622/) (2022)
- [Towards IID representation learning and its application on biomedical data](https://mlsanity.com/paper/2203.00332/) (2022)

## Built on by (most cited first, 3 of 3)

- [Targeting Negative Flips in Active Learning using Validation Sets](https://mlsanity.com/paper/2411.10896/) (2024)
- [BALD-SAM: Disagreement-based Active Prompting in Interactive Segmentation](https://mlsanity.com/paper/2603.10828/) (2026)
- [Active Learning for Stochastic Contextual Linear Bandits](https://mlsanity.com/paper/2605.24803/) (2026)

## Abstract (arXiv)

This paper presents a novel approach to active learning that takes into account the non-independent and identically distributed (non-i.i.d.) structure of a clinical trial setting. There exists two types of clinical trials: retrospective and prospective. Retrospective clinical trials analyze data after treatment has been performed; prospective clinical trials collect data as treatment is ongoing. Typically, active learning approaches assume the dataset is i.i.d. when selecting training samples; however, in the case of clinical trials, treatment results in a dependency between the data collected at the current and past visits. Thus, we propose prospective active learning to overcome the limitations present in traditional active learning methods and apply it to disease detection in optical coherence tomography (OCT) images, where we condition on the time an image was collected to enforce the i.i.d. assumption. We compare our proposed method to the traditional active learning paradigm, which we refer to as retrospective in nature. We demonstrate that prospective active learning outperforms retrospective active learning in two different types of test settings.